9 papers
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Yuankang Zhao, Youngsoo Baek, Felipe A. Medeiros +2
Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largel…
NEST: Nested Event Stream Transformer for Sequences of Multisets
Minghui Sun, Haoyu Gong, Xingyu You +3
Event stream data often exhibit hierarchical structure in which multiple events co-occur, resulting in a sequence of multisets (i.e., bags of events). In electronic health records…
Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment
Zigui Wang, Minghui Sun, Jiang Shu +3
Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in structured data fields. This a…
Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing
Yuqi Li, Quinn Lanners, Matthew M. Engelhard
In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a…
Interval-Based AUC (iAUC): Extending ROC Analysis to Uncertainty-Aware Classification
Yuqi Li, Matthew M. Engelhard
In high-stakes risk prediction, quantifying uncertainty through interval-valued predictions is essential for reliable decision-making. However, standard evaluation tools like the r…
Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning
Minghui Sun, Matthew M. Engelhard, Benjamin A. Goldstein
Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visit…